Investigation and prediction of the changing trend of climate parameters on Discharge (Case Study: Godarkhosh Subbasin)

Document Type : Research Article

Authors

1 Graduated student, Faculty of Agriculture and Natural Resource, Ardakan University

2 Assistance Professor, Faculty of Agriculture and Natural Resource, Ardakan University

3 PhD Student, Water Engineering Department, Isfahan University of Technology

Abstract

Predicting of the river discharge is one of the most important issues in the planning and management of water resources in terms of energy generation, water allocation and use of water resources. In this study, the trend of climate parameters such as temperature and precipitation and so discharge as a hydrologic parameter were investigated based on the Mann-Kendall test and then was modeled and predicted the discharge of monthly streamflow Godarkhosh in the period 1992-2012, using linear time series SARIMA. To investigate the static model, tests autocorrelation (ADF) and partial autocorrelation (PACF) were used. The results of the Mann-Kendall test indicated the temperature parameter has an increasing trend and precipitation and discharge parameters showed reduction trend at all stations of the studied area at 99% confidence level. Also, the result of time series model indicated that the time series data of river flow were non-static data that with the differencing method, the non-static data transformed into static data. Then different models were fitted into static series to estimate the river flow and finally, model ARIMA (1, 0, 0) (0, 1, 1) was selected as an appropriate model to predict discharge and the river flow was predicted for 4 years from 2013 to 2016. The results showed that the discharge has increased at the end of the period, in which the model estimated river flow as well based on rainfall data from Meteorological Organization in the 2015-2016 year. Therefore, application of time-series models in water resources management can be useful.

Keywords


Abdolahnezhad, K., (2015), Forecasting of Monthly Sum-raining by Stochastic Models in Time Series. Geographical Planning of Space Quarterly Journal, 5(17):15-25. [In Persian]
Alizadeh, A., (2011), Principles of Applied Hydrology. Imam Reza University Press. Thirty-second printing.800 pages. [In Persian]
Ansari, M., Noori, G.H., Fotohi, S., (2017), Investigation of Temperature Precipitation and Flow Trend Using Nonparametric Mankendall (Case Study: Kaju River in Sistan and Baluchestan). Journal of Watershed Management Research, 7 (14):158-152. [In Persian]
Banihabib, M.E., Ahmadian, A., Jamali, F.S., (2017), Hybrid DARIMA-NARX model for forecasting long-term daily Inflow to Dez reservoir using the North Atlantic Oscillation (NAO) and rainfall data. GeoResJ. Volume 13: 9-16.
Barnston, A.G., (1991), An empirical method of estimating rain gauge and radar rainfall measurement bias and resolution. Journal of Applied Meteorology and Climatology, Volume 30: 282–296.
Bashari, M., Vafakhah, M., (2010), Comparison of different time aeries analysis methods for forecasting monthly discharge in Karkheh watershed, Journal of Irrigation engineering and water, 1(2): 75-86. [In Persian]
Box, G.E.P., Jenkins, G.M., (1976), Time Series Analysis, Forecasting, and Control. Revised ed., Holden-Day, San Francisco
Dodangeh, S., Abedi Koupai, J., Gohari, S.A., (2012), Application of time series modeling to investigate future climatic parameters trend for water resources management purposes. Journal of Water and Soil Science, 16 (59):59-74. [In Persian]
Esmaeilpour, M., Dinpazhooh, Y., (2012), Analyzing long-term trend of potential evapotranspiration in the Southern parts of the Aras river basin. Geography and Environmental Planning Journal, 47(3): 49-52. [In Persian]
Fathian, F., Fakheri Fard, A., Dinpazhoh, Y., Mousavi Nadoshani, S.S., (2016), Performance Evaluation of Linear (ARMA) and Threshold Nonlinear (TAR)Time Series Models in Daily River Flow Modeling (Case Study: Upstream Basin Rivers of Zarrineh Roud Dam), Journal of Water and Soil, 30(5):1440-1460. [In Persian]
Karamouz, M., Iraqi-Nezhad, Sh., (1394). Advanced hydrology the Amir Kabir University of Technology Publications. Third edition. 480 p. [In Persian]
Kavian, A., Maryam Namdar, A., Mohamad Golshan, M., Bahri, M., (2017), Hydrological modeling of Climate Changes Impact on flow discharge in Haraz River Basin. Journal of Natural Environment Hazard, 6(12): 89-104. [In Persian]
Kendall, M., (1975), Rank Correlation Methods, Griffin, London.
Khalili, K., Fakheri Fard, A., Dinpajooh, Y., Ghorbani, M. A., (2011), Nonlinearity Testing of Stream Flow Processes by BDS Test (Case study: Shaharchi River in Urmia, 21(2): 25-37. [In Persian]
Khalili, K., Nazeri-Tahroudi, M., (2016), Performance Evaluation of ARMA and CARMA Models in Modeling Annual Precipitation of Urmia Synoptic Station. Water and Soil Science, 26(2-1): 13-28. [In Persian]
Kim, B., Hossein, B., Choi, G., (2011). Evaluation of temporal-spatial precipitation variability and prediction using seasonal ARIMA Model in Mongolia, KSCE Journal of Civil Engineering, Volume 15:917-925.
Manjushree, R., Shinde, V., (2011), Application of software packages for monthly streamflow forecasting of Kangsabati River in India. International Journal of Computer Applications, Volume 20, number 3: 7-14.
Mann, H. B., (1945). Nonparametric tests against trend. Econometrica. Volume 13:245-259.
Naveh, H., K. Khalili, K., Alami, M. T., Behmanesh, J., (2012), Forecasting River flow By Bilinear Nonlinear Time Series Model (Case Study: Barandoz-Chay & Shahar-Chai Rivers). Journal of Water and Soil, 26(5): 1299-1307. [In Persian]
Niromand, H., (2007), Time series analysis: univariate and multivariate methods. Second Edition, Ferdowsi University of Mashhad, Institute Press. 602p. [In Persian]
Niromand, H., Bozorgnia, A., (2012), Introduction to time series. Ferdowsi University Press, Mashhad. First print, 289 pages. [In Persian]
Rahimi, L., Dehghani, A.A., Ghorbani, K.H. Abdolhosseini, M., (2014), Comparative Analysis of Time Series Models for Total Flow, Base-Flow, and Runoff (Case study: Chehelchai River, Golestan Province). Journal of Water and Soil Conservation, 21(3): 55-77. [In Persian]
Sharifian, H., Habibi, A., (2013), The Effect of Climate Change on the Changes in Surface Water Resources in a Part of the Golestan Province, First National Conference on Water and Agriculture Resource Challenges, Isfahan, Iran National Irrigation and Drainage Association, Islamic Azad University, Khorasgan Branch, pp. 30-21. [In Persian]
Torabi, H., Emamgholizadeh, S., (2015), Trend Analysis of Streamflow changing of the rivers of Lorestan Province with MKTFPW. Journal of Applied research in Geographical Sciences, 14(35): 73-94. [In Persian]
Valipour, M. Banihabib, M.E. Behbahani, S.M.R., (2013), Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology, Volume 476: 433-441.
Yue, S. Pilon, P. Phinney, B. Cavadias, G., (2002), The influence of autocorrelation on the ability to detect a trend in hydrological series. Hydrological Processes, Volume 16, number 9:1807-1829.
Volume 7, Issue 17 - Serial Number 3
September 2018
Pages 137-154
  • Receive Date: 05 March 2017
  • Revise Date: 11 October 2017
  • Accept Date: 27 December 2017
  • First Publish Date: 23 October 2018
  • Publish Date: 23 October 2018